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Explain the vertical axis of the benchmark timing graph #1102

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Dec 2, 2020
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Explain the vertical axis of the benchmark timing graph #1102

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2 changes: 1 addition & 1 deletion benchmarks.md
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Expand Up @@ -6,6 +6,6 @@ It is important to note that the benchmark codes are not written for absolute ma

![Benchmark results](/assets/benchmarks/benchmarks.svg)

The benchmark data shown above were computed with Julia v1.0.0, SciLua v1.0.0-b12, Rust 1.27.0, Go 1.9, Java 1.8.0_17, Javascript V8 6.2.414.54, Matlab R2018a, Anaconda Python 3.6.3, R 3.5.0, and Octave 4.2.2. C and Fortran are compiled with gcc 7.3.1, taking the best timing from all optimization levels (-O0 through -O3). C, Fortran, Go, Julia, Lua, Python, and Octave use [OpenBLAS](https://github.com/xianyi/OpenBLAS) v0.2.20 for matrix operations; Mathematica uses Intel® MKL. The Python implementations of matrix_statistics and matrix_multiply use [NumPy](https://www.numpy.org/) v1.14.0 and OpenBLAS v0.2.20 functions; the rest are pure Python implementations. Raw benchmark numbers in CSV format are available [here](/assets/benchmarks/benchmarks.csv) and the benchmark source code for each language can be found in the perf. files listed [here](https://github.com/JuliaLang/Microbenchmarks). The plot is generated using this [IJulia benchmarks notebook](/assets/benchmarks/benchmarks.ipynb).
The vertical axis shows each benchmark time normalized against the C implementation. The benchmark data shown above were computed with Julia v1.0.0, SciLua v1.0.0-b12, Rust 1.27.0, Go 1.9, Java 1.8.0_17, Javascript V8 6.2.414.54, Matlab R2018a, Anaconda Python 3.6.3, R 3.5.0, and Octave 4.2.2. C and Fortran are compiled with gcc 7.3.1, taking the best timing from all optimization levels (-O0 through -O3). C, Fortran, Go, Julia, Lua, Python, and Octave use [OpenBLAS](https://github.com/xianyi/OpenBLAS) v0.2.20 for matrix operations; Mathematica uses Intel® MKL. The Python implementations of matrix_statistics and matrix_multiply use [NumPy](https://www.numpy.org/) v1.14.0 and OpenBLAS v0.2.20 functions; the rest are pure Python implementations. Raw benchmark numbers in CSV format are available [here](/assets/benchmarks/benchmarks.csv) and the benchmark source code for each language can be found in the perf. files listed [here](https://github.com/JuliaLang/Microbenchmarks). The plot is generated using this [IJulia benchmarks notebook](/assets/benchmarks/benchmarks.ipynb).

These micro-benchmark results were obtained on a single core (serial execution) on an Intel® Core™ i7-3960X 3.30GHz CPU with 64GB of 1600MHz DDR3 RAM, running openSUSE LEAP 15.0 Linux.